{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"/data/iris.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [\"SepalLengthCm\", \"PetalLengthCm\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-versicolor    50\n",
       "Iris-virginica     50\n",
       "Iris-setosa        50\n",
       "Name: Species, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Species.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "colors = [\"red\", \"green\", \"blue\"]\n",
    "\n",
    "for i, v in enumerate(df.Species.unique()):\n",
    "    df[df.Species == v].plot.scatter(features[0], features[1], label = v\n",
    "                                , ax = ax, color = colors[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import *\n",
    "from mlxtend.plotting import plot_decision_regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 1.0\n",
      "precision: 1.0\n",
      "recall: 1.0\n",
      "f1_score: 1.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1eb177f0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kYevCv2tb0rfIFP+j0BWRIinK6uIeQ8bwxfuvEdmiL/aUWAKjq1JpwG1YQ8txZO9qt/ft3bqOlp17ExAY5LLdsFgIDI1g77Z1RQpdvRYkxU2hK1LGOZ1Odm9axZmjBwkKCaPVZX2IiIr5/+0OB7s2riT2+GGCQ8NodVk/wstFF9vzDcMgokWfS7rHNE0MS/5LUtztOCXiKwpdkTJs18ZVzHrtccIiIqnbrC1pyQnMfuc5OvUfxrWTHmX3ptV88cYTRERXoE6TVqQmxvPN1OfoOnAkI29/CJstwCd1N2jZnu8/nYLdnuuy3TRNcjJSadCyvZcrE8mbQlekjNq3bQMfPXMv4//7Js06dDv/8/SUJD55/j+89Z9xnDy8j4lPTqFx2y7n21OTEvjo2fuZ9cpjjH3kZV+UTrW6jahauz6Lv/7IZfvZLb8ABo3bdvVuYSL5UOiK+LnCblU4d/qrjLnnSd5/+n7szgunYU3TxJGWwKg7HuadJ+9x3Z6+iv6jx1O9XuMi1W/m5nBoyg0X/dxwOPK8b+wjL/Pq5OvJyspi77u3Yg0IwpGbQ1ZqItnpqVSt11Qrl6XEUeiK+LnCbFUYe/wwcSeP0q7nlXzy+tPUvmvmBe05cUc49uEkzp46jt1pXtQOcPC1Uaz++VuuufPRItVfvmqNQm21WL5SNR55/zuWzZvFqh9nk3D6KBFRMfQcdA19R/6L6IpVilSXiCcodEXKoKT4WCpWq4XV5vqPAHtaAlgsJCfEue3DsFhJdnNKj7dERJVnyNjJDBk72ad1iBSUQlekDIqMrkD86eM43UzhWkPLgdOZ91aKTgcR0THu24tBVkY6q36czeqfviUh9iThUeXpfPlQeg27/oIV1iL+QqErUgZVqVWPcjGV2L5micv2gJiagEnlmnVdtpv2XJy52XS5YoTHanQ67Lx817VUqFqTa+96jGp1GxJ/+gTLF3zJsxOGcv8bs9zWJ1JSKXRFyqhhE+7nk+f/g+m48LUbZ04WcT++BRYrv3z1IY7sTI59cs/5dtPpxJ58BgwLtRu18NiZs6lnT9NuyLXs27mdmW89e0FbrhOeHj+EqYt2aLGU+BWFroifK+xWhc079uCG+57ivScmcfDlYWANANOJmZuDYQsgKDyKa+96jOlP3QsWKwEVauHMTCPryDaCa7ci+8RuoHALufKr35Gbgz07g6G33MfTt19z8TaPpsmmpwazZ/Nqmrbvhoi/UOiK+LmijCbb9ryCdxfvYuvKXzlz7NyOVG2696dC1Rrnr5k7412iOo8iK/4E1qBQooffRVBU5WI7yN1V/WsWzeWPtcsICg5xeY9hGASFRbB36zqFrvgVha5IGWcLCKRDn6vcthuGhZhWl7ZNY5EVcJtHp9PphWJEio/O0xWREqdei7bs3rSK3Jxst9fkZKRpm0fxOwpdESlxKteoS62GzVj05TSX7fE7lmE6HTTv1MvLlYkUjaaXRUq5oq4uzm+hlqfOnL35wRd59Z7ryMzIOL/No9OeS2ZqEllpSVSt2wSLxeKx1dMinqDQFSnlirq6OL/g8lSwRVeswiPvfcfSuZ+x6sfZxJ88THi5aLoNGE6/UWOJqVIdKPrnE/Emha6IlFjh5aLPb/NomqbeyRW/p+90RcQvKHClNFDoioiIeImml0UkT5OHdr7oPF0Am8VgyoJ1PqhIxH8pdEVKuaKuLnZ3nu6RqTcXubbi4KnV0yKeoNAVKeVK+2szpf3zSemi73RFRES8RKErIiLiJQpdERERL9F3uiKSJ5vFcLloymY5995sUbdh1DaOUpYodEUkT/m9FlTUbRi1jaOUJZpeFhER8RKNdEU8yDRN/li3jN/nfcGRvX9gCwigRefe9Ln6JqrVaejr8vxeVkY6axZ9x+qf5pAYd5qIqBi6DBhO98GjCYso5+vyRC6ika6Ih5imyRdvPMnsd16gbc8reOT9b7nnlU+JLF+B1+65no1Lf/R1iX4tNSmelyZdw66NKxl5+0M8Pn0BN9z/NMcP7OG5W4dx9tRxX5cochGNdEU8ZNWPszm8ZxuxZ87w2dQXLmgznQ6m/W8ydZq0ZPpzD+a5kMjTC42K2n9y/FlOHN7n8ueeNOPFh2nZpTd7tm/msynPX9CWmZ3N/8Zdxds/bdNBCVKiKHRFPMA0TX6b8ynXTHqMKU9MpuZdsy665shrI1k27/N8FxJ5eqFRUfs3nXbiF77u8ucFUZhtHGOPH+bQ7q3c9tRUNt028qL6TaeTjU8N4uDOzdRv0b5AdYh4g0JXxAPSU5JIiD1Jk3Zd3V5j2AL4c8saL1blGVEVqxQptAszWv9z6zpadOlNQFCQy3bDYiEoLII9W9YqdKVEUeiKeIBpOrFYrPlObTpdnN5zcV8macd2k3H6EIYtgKiGHQgIjy6uUjFNk5RD28k6ewxLYAhRjTpiC/HeYQEOu509m1cTf/oEYZFRtOjci6CQ0DzvMZ1OrNa8//gyMDCdzuIsVaTIFLoiHhAWGU1oRCSHdm11e43pyKV+87bs3LbJ7TVH9v5B0slDpM5+kcg6LXFkZ3Bk4VTKt+hZLIHy59Z1JJ44SPqCKYTXbIo9I5nDC6ZQscNATDP/vxAU1abff+abqc9SLqYSNeo1JjHuFLNee5wBY27lyutvc/uXlnrN27Fw5lQcdtdT2KZpkp2ZRr3m7TxZvsglK1DoGoZxGEgFHIDdNM0OnixKxN9ZLBZ6D7+JeR++5jK87MmxkJtD7+E3uA1de042U/4zjpByMTSd/AmG5dzLBvbMVA7Nf5O02OM4nU4slsK9hHDgj8188MRdhJWvRJO7p58PuNy0RA7MfpGss6cK1W9BbVm+iK/eeorbn556wRTw2VPHeP+JSeRkZTBs/P0u761RvzEVq9ViyXcXHzkIEL99CZhmntP7Ir5wKSPdPqZpenY5oogfcrf6NywsnKioKMzMNI6+ORr+ng6152DmZmHYAqlWt5HbhUQ5aUlcdcPtfP/5NDa/fN0FbaZp4szOYs+m1TTr2L1Qdc+d/irX3vUYX0x9gc0vjbmof0d6Ksf27aJmw2Z59lOYhVBOp5PZ773IhCfe5Jv3X73o9+d02Plp1vv0Hn4T5WIquuxj3CMv8+o915Oba2fvuxOxBgTitOeSmZpEVloSVes2LfRfSEQ8RdPLIkWU1+rfh96axeqf5rBs3uccP7gHw7DQsktv+o0ae34U5mohUXpqMo+O6UX3wdey7Ke5Lvvf+fqNrPpxdqFCN+7kUU4fPUCHvoP4/osPXfa/45UxrPppDmMaPpFnX4VZCLVv+waCQ8No1LqT29/fthdGsm7xPAaMudVlHxWq1uTR9+fy25xPWfXTbJJOxhMSHsllV4zg8mtuIaZK9UuuS8TTChq6JvCLYRgm8IFpmtM8WJNIqWG12egxZAw9hozB6XBgWCwFem80JeEsEVExhIS5Hy1aAwJJjDtdqLoS405TsXptbLYAt9fYAoMK3X9+kuJOU7VW/Tx/F9YCPD+yfAVGTHyAERMfwGG3Y7VpHCElW0HnXrqZptkOGAhMMgyj5z8vMAxjomEYGw3D2Lh8gU4GEfknizX/1cx/Cy8XRVpSAjnZWW6vcdpziYgqX6haIsqVJ+HMSZx5LMZy5Ba+//yEl4sm/syJPK9x2nMJv4TnK3DFHxQodE3TPPnX/4wF5gKdXFwzzTTNDqZpdug59Lp/NovIJYiIiqFuszZs+G2hy3bTNMlMTaLzgOGF6r9K7fpERJXnj3W/u+7f4SArLYnO/QvXf34at+tK/OkTHNu/22W7IzuTrLQUOvUb4pHni/hKvn81NAwjDLCYppn61/8+AHja45WJlBC+Ou91yNjJvPPoRCz/eGfWdDg4umgaYNL6sn6Fqs8wDIaNv4/PXnmMlLR0bP8n/ExHLsmrvsLhdNKgpWc2lrDZAhg67h4+ePIuHFgvaLNnpbP/q2cICougYrVa+fal83jFnxRkPqYyMPevaTEb8IVpmj97tCqREiS/bRILs3q3IOo1b8u4R1/lnUdvY/MzQwkIDsV0OslOT8EaGES1ek2x2myF3saxZZc+XDPpUaY/dS+nv3gIS0AwptOBMysNS2AIhi3Yo/sW9xgyhpzsLGa/+zxbnhuBLTAIp8NOdnoKQWGRVKndoED96Dxe8Sf5hq5pmgeB1l6oRcQveXI01aJzL97+eTtbV/7KiYN7sAUG0fqyftSo36RY+u/YdzAfv/YUMX3GkRN/HEtgMGGNuhEYU50jU28ulmfkpd+osXS9ciSblv5A/JkThEVE0aHPVURXqurxZ4v4glYeiJRwtoBAOvS5ig59rvJI/4ZhENGir0f6LojQ8Ah6DBmT/4UipYDeHBcREfESha6IiIiXaHpZJB+eWij1t/xW3+bXnl99+d1vsxguv7+1WUrG4e9F/fwiJYlCVyQfnn7tpKiH2OdXX373T1mw7pJr9qaifn6RkkTTyyIiIl6ika6IH3M6nWxfs4Tl87/gxKG9BAaH0Kbb5fQefmOBNvx3OhxsWfELyxd8yeljBwkOCaNdryvoOfR6oitW8cInEClbFLoifso0TT55/gFOHPyTAWNupUHLDmSmpbB28Tyev204tz45Jc/zZE3T5L0n7iT5bCz9R4+nXrO2pCUnsPqnb3n21qFMen4a9Zq18eInEin9FLoifiozOYHEuNPExcUxc8rzF7Q57XZev/8m3vh+k9v7MxLjMKtWA1sg82a+f0GbERjCK3eP4c2FmwkKCfVI/X/z9DaO2iZSShKFroiP5bf61lW7aZpkpSUx+q7HeeG+sdS+a+ZF9x98ZQSrf/rW9f1OJ1lpyVx79+O89dgklwuVtjw7nA1LFtJ90LVF+Xj58vQ2m9omUkoSha6Ij+U32nLVfurwfqY+eis1GzZze59hC2L3xpUu7z+4cwtfvPkklarXdnt/UFgEuzeu8njo5kejUSlNFLoifsjpdGC1njuA3jRNsk7uJefsESwBwYTUaYM1JAIM3J6X63Q6sOZxgD0AhpHnebt/M02Tfds3EHfiKCHhETTv2OOCKWmn08m+bes5e+o4oRGRNOvYg6DgkIJ/WJFSRKEr4ocqVa9DWnIiW1YsxpGRzJn5LxFcoxnOrDTifnqbiJb9cOZmU695W5f3V6vbmDNHD5IcH+f2GTkZafkupNq1YSVfvvU/rDYbtRu1IDnhLJ+98ij9Ro3lqpsmsXP9cr6e8jSBwSHUbNCUpLOxzHzlUQZcO54rb7jdo6cYiZRECl0RPxQQFESry/oy/anJGAHB1Lp9OoZx7rV7e3oSZ+a9CPYct1PDoeERdOw7mAUfv4Fpmhe1p5/YR05mGpcNHOm2hj2b1/DRs/cx7tFXad6p5/kAjT99gulP38Ox/bs58Mcmxj/+Ok3aXXa+Pe7kUab9bzJZGemMmPhAUX8VIn5FoSvip1KTEoiqUJmzZ05y6M3rwPLXYfD2HMycTMAg4cwJyrs5Jm/EbQ/y+n03kJkYy5/vTMAWGHx+gVVG0lkq1qhHWGSUy3tN02T2uy9w47+f5fvP3ufL9165oN1ht3Nw5xb+9dBLzPtkKmlvv3BBu9NhZ9FX0+g9/AaPb+OobSKlJFHoivih1KR4DuzYyDOf/8oT4wZjNwLISToDQFTjLlTtPoojc55n5Q+zadCyg8s+QsMjeOCtL1ny7QxWLPyKxOMnMAyDNj36M2D0BOo0aeX2+ccP7CEjNZnW3fsz5+O3L1odnHZsD4nTJnP21DG3q4e3vTiKNYvmenyhlBZiSUmi0BXxQ0lnY4mqWIWIqBhCy8VQb8LbOO05GBYbhuXcNLMtIIjE2FN59hMcGsZVN93JVTfdSW52NtaAACyW/HeHTYw9RdXa9d1em50ci8UWQGLcabd92AKD8mwXKY0UulLqHd69naVzZ7L/j00YGDRq05k+I27K83Wb/2vf9o0sm/sZh/Zsx2Kx0LR9N/qMuJFqdRt5uPJz07h/rPud5Qu+5MTBP7EFBNLqsr607TGA5IQ47Pbc89dabIEX3Otw5BJWzvX0sCsBQUEX/cxht7NlxS+s+P4rYk8cITg0jPa9BlKrUQsSYk+6/D4YICA0EtPhIDyP5zvs9jzbRUojha6Uaou//ojF33xEv1FpDwxlAAAgAElEQVRjGXjjnZhOJ1tW/MJbD45j6Lh76Tn0ujzvX/DJW6z+aQ79rx3P0FvuxZ6bw6bff+a1e2/g2rsep3P/YR6r3el0MuvVxzjwx2b6j57ANZMeJSs9jXW/zuedR28jKqYSm3//2eW9pmmSlZJEl/7DC/383Jxs3n9iEqlJCfS/9hbqNGlFamI8q36aw9J5s7DabBz4w/WOV2HVm+B02KnVoDlb1q26+LPl5pCVlkSnfkMLXZ+IPzLc/U21KKYvP1j8nUqJVVK32duzeTWfvvgQYdGVyMrOvqDNkZtD8umj/Oftr9y+FrNlxS9898HLBIVHkZmVdUGbPSeb5NNHeeLjhcx49QmPfP4l385g/W/fc+LYURz/+C/KtOfiyEwhIqo81tBIGk/68P+3ORwc/fkDzm76iak/beOle24sVH1z3nuBM8cPk5yURHp6+gVt2ekppMafpnzFqjhtQTS644Pzbc7cHA58+xLJf64jpmJl7IaNhv+n3ZGTxYFvXiDj6A7e/nHLpf5aREocw4AJPeoV6P03jXSlyErqNnu/zZnBoJvv4uc5n7ms74/XrmfpdzPdhu5vcz5l2Pj7mTvjXZf373hlDMvmzvLI53c6nfz27aeMf+x1Xn3oNjfbPF5Ns4492LDkB7Y8O4yA4FCcTifZaclYA4OoVq8pFqu1UPVlZ2aw6sc5PD59Aa8/fJvL+zc/M4wmbbuw5pe5bHluBAFBwTgddrLSUggMCaNGwxZ07XcV30x9jq3PX40tMAiHw052WgqBoeFUqeP56XmRkkahK6XWro0rGffIK/w85zOX7UFhkezauNJlmz03h/07NnHvazOYO+NdN/dHnLs/oPh3V4o/fZzcnBzq5rE5hREQSHJ8LFN+2samZT9x6vA+AoKCadPt8gJ/X+3O4T93ULlG3TyPBwwKi8BpOnnj+01s+G0hcSePEhIWQfs+A6lco+7567oMGMGGJQs5e+oYoeGRdOg7iIrVahWpPhF/pdCVUst0OrEGuN/q0Pg/2xzGHj/Mod3bMAyDhq07ER4ZjWEYWK15/CdiWHA6c/HEnkpOpwObLSCfHZvO1R8YFEzXK0YU6/NNpyPP3x389ftzOAkJi8jzu/GQ8Aiq1mlAQFAQoRHlKBdTqVhrFfEnCl0pteo2bc3O9cvdtudkpFGzXhOmPHgLR/7cQZN2l+FwOPjizf/RrGN3Kteow57Nq/O8v3nbThw6uL/Ya4+pXJ2c7ExOHTng9hrTnkPdpq2L/dkA1es14cSBPaQlJ7q9JiczjbrN8n7+zvXL+WrK09gCAqlRvynJ8WeY+dIjDBgzgSuum6htIKXMUehKqdV7xI38MGMqpotN++1ZaaQnneWk007v4TdwxzPvnX9lJisjjbnTXiUrM515H72BaV58f256EpnJ8fQecSOHXvtfsdduCwikx+DRzP/odZev5eQmnsK0Z+e7+rqwIqLK07pbPxbOeNvl89OO7yEnM4MuA9yPsHdtWMnHzz/A+Mdep2mHbucDNvb4YaY9NZmsjDSGT/i3R+oXKakUulJkJXWbvQ59BrF74yrWL1nI7iljCQwJB86NUNMT4wgICKRll97s2LCaNUsXXXCvaZpkpqcTEZVLauwJ9rx9CwHB507OyU5PJSMpjsiYSjRo2cFjn3/gjXcy5cFbMLNSOfzWdWA595+rac/GmZ2JLTisQN+N5lefu9XnIcHB2LPSyUyKZ++7t2ILDMbpcJzfJrJSzXqEuvmMpmnyzTvPMfbhl5g/4x0+f+fFC9qdDjs/f/4BvYbdwPtP3VciV7+LeIJeGZJSzTRN1i6ay5LvZnL8wB5M06ROk5b0HfkvZr/zPPe9NoN3nrrf5ercPVPGER0dTY/Bo1k6bxanDu/HNE0atGxPv1FjadtjgMfrz83OZtm8Wfw+/3MS4k5hOk2ad+zB5dfeQpN2XYvlGY+NHex2dfNjU7/ktzmfsGLh16QlJwDQtvsABoyZQO3GLd32eWj3Nj5+7t88/dliHh83xO02kAPHTGDlrz+4ff5zny4swicT8Q69MiTyF8Mw6Hrl1XS98mpyc7IxDANbQCBOh4OPnrmPqnUaur3XGhhEQuwpegwZQ48hY8jNzsawnLvfWwKCgug/ejz9R48nJzsLq9WG1ea9/2xDwyMYMnYyg/91NznZWQQEBGKxWvO9L+HMSarVbZTnd7a2v36/ImWJQlfKjIDA/7/NocVqJTgsnKQ89v51/mObQlfbJHpTYFCwz55tGMYlHTwfFhlFwpmTeV7jsNsJiyxX1NJE/Er+O5uLlFKd+g1l+ffuvzPMSk2k8+We2+axNGvYqiMpCXEc2fuHy3Znbg7ZaUl0ulzbQErZopGulFn9R4/npUnXYPxjcwvTNInb8AM5mel0HzzaR9UVn6Ju01mY+602G0PG3cP0p+7BwYXT0c7cbPbPfoGA4DCq5TG9L1IaKXSlzKpUvTZ3v/AhL989mk1PDyEwNBxMk+z0cwFTrX5TIqLK+7jKostvG8j8VjcXdpvL7oOuJSsjnTnvvvDXNpDBOB2557eJ/HsbyJK6+l3EExS6UqbVadqKt3/ewY61Szm4cwuGYaFxu640bX9Zmdm4wZOv5Vx+zTi6Xnk1639dQPzp44SER9Khz1UXbBOp14KkLFHoSplntdlo070/bbr3d3tN7PHDHN6zHYvFSsPWnSgXU9GLFfq3sIhy9Blxk6/LECkRFLoieUiIPclnrzzK0b07ady2C/bcXGa99jitLuvLdff+j5AwTYGKSMEpdEXcSEk8yyt3X0fPIWO487kPzr9ylJGawpz3XmDKg7dw/xuzLngVSUQkLwpdETcWf/0RLbv0Zuva5az89YcL2kzTJC3uBBuX/MCX776E3XnxJmw2i8GUBevyfU5RVxfnp6gLlbTQSaT4KHRFXDBNk1U/zuHhd2fz1uN3uVy9u/utsaz8cTZ2p+nykPkjU28u0LMKuzq4oIoa3FroJFJ8tDmGiAs5WZlkZ6ZTqUYdt9fYAoNIOHPCe0WJiN/TSFfEhYCgYAyLhdSkeLfXOOy5lIuMIinlGGcWvEL2qf1gsRJaty3l2g3yYrUi4i8UuiIuWCwWOvQZxMqF37i9Jis1iarVa+DI3E1QlYZEXzYa055D2u4VHJ/5b3BxDq+IlG0KXRE3rhhzK6/ecx2ZOQ5OHN53/uemaZK+eznZ6akknDmJERhC6s6lpO5cev4aS2gk9vgTxJ44wkcvPKzzYkUEUOiKuFW1TgNuf+Y9Xpk8hpMz7scSFAqmiSMr7dwFFgsjb3+IaS88QszAey66//SsB/h9/udF3oZRREoPha5IHhq26kCF2o2J6notacd2Y1gslGvQnrAaTdj0zDBade1L+UpVqdWg6UX3Zlasyq4NK8lvvaJGuyJlh0JXBDh76jiHd2/DsFho0LLDBds8GoZBTIuexLToef5n9qw0DIw8D3Q3DAOnwwEWC5lxx8g4tR/DaiOibisCQnWOrEhZpNCVMi0pPpZZrz7OgZ2badymMw6Hnc9eeZSWXc9t8xjqZorXGhSGxWbj4M7NbvvOyUyndvM27NywkqQP7yeiTkucudkc/O41Ylr1xnRqoZVIWVPg0DUMwwpsBE6YpjnYcyWJeEdaciKvTr6Ojn0Hc+uTbxEUfO5c3Yy0VL774CXe+s9YHnjzC5f3GoZBcEQ08z9+E9O8eDeq3LREMpMT2L9jI4Gh4TS7ZwYWW8C5tvQkDn8/ldTY4zgdjjxHyyJSulzKSPceYDcQ6aFaRLxqybczaNCyA7/N/4pFcy/8XtU0TcysVNYunud2oVPFajUJDAwmLe4kf04djy3oXGjnZKSSnhCHLTCI7lddw7IfvmXLqzde1L8zJ5vta5fSptvledaZ3zaRnt5GUkSKT4FC1zCMGsAg4Dng/vyuX/vVG3S6ZrL+Bi8l2sofZzP5pY9Zv3Kpy20cD705hpU/fMMj733rtg+7PZff533Osvmfk3j6CE6ng7pN29Dnzof47NXH6DvyX2xas9zl6uVdb9zEyoXf5Bu6+a1+9vQ2kiJSfAo60n0TeBAo0DsM93WL5INPHyQzuhGNew4nqkLlQhco4glOh4Pks2eoXq+R22sMi43403lv82izBdBv1Fj6jvwX2ZnpWKw2AoOCSYqPxRYQmOf/79sCg7WNpEgZk2/oGoYxGIg1TXOTYRi987huIjAR4IMHR/P2bX1Zv+cEc354hZ3BdWjScxgxVWsWW+EiRWGxWgkKDSPp7Bn3F5kOwiIrcHjPdpbNncWhv1Y3N+vQnd7Db7hgX2bDMAgODT//7yGh4eRkZZKVkea2e4cjl9CISsXxcUTETxRkpNsNGGoYxlVAMBBpGMYs0zQv+JLKNM1pwDQAVr9tAnRqUp1OTapzOj6FF7+bwkFrJGZoeTpcfQcWi85aEN/q1G8IKxZ+7bbdmZtNdIXKvPv4HfQb+S8uv3Y8DnsOm37/mRfvvIbr73uKDn2ucnlvUEgozTp0Z+2ieW77z0pNovNNdxT5c4iI/8g3dE3TfAR4BOCvke4D/wzc/FSJieTNW3sDsGXfST78+EEclVvQqMdQIqLKX3LRUrZ4aqFQ/9HjeXnStTjtuRf83DRNUnf8imnP4czxQ0TEVGH5ogUsX7Tg/DUB4eX48Jn7qFG/CVVq1XPZ/8Ab7+Dth8aTmeO8aBvJtO2LycnKpNPlQwtdv4j4H6+/p9u2YTXeaViNFX8cYc63z5JevimNLhtEdOVq3i5F/ISnFgpVrlGXO5/7gJfuGs2Bl4dhsQYCJk57DmBiGFZGTPwP8z/7wOXzd7w8mmXzZjFm8hMu+6/TpBW3PPYabz14Cydn/htLYCiYzr+2kTSwBYcTFBKab535bROpbSRF/Mclha5pmsuAZcXx4B4tatO9eS2OnUnkpXmvszcoCltEJdoNuxXDMIrjESL5qt+iHe/+uottK3/l4M4tGBaDxm270rhdV+4Z2Jp2Pa9g/mcfuLw3KDySneuX59l/8049qVCrEZEdh5/bkcpiI6pxJyLqtOLQR5MLVGN+o3m9FiTiP3y6I5VhGNSqUp53bu8DwNrdx/n0w/9gqdmext0GERqhV4LF82y2ANr3Hkj73gPP/yw3OxvDMLD+taGFS4YFhz0n3/4Ni4WKbftD2/7FUa6I+LEStQ1kl6Y16NK0Br9uOcS3X/wXZ/X2NLrsKiLLV/B1aVLGBAQFUalGbfZuXef2mtzMNOq3au/FqkTE35Wo0P3b5W3r0q9NHfYdi+PV2c/jDI0hMLo6bQb9S1PP4jW9h9/E959OcbnNoz0jhYzkBHoNv6Q1hSJSxpXI0IVzU8+NalVi2qR+ACzbfoQvPnyQ4PpdadT1ygItQJHSwdMLhdytjg4LCycqKoqj+3axZ+p4As5v85hGemIsEdEVaNymc779a6GTiPzNcPW3+CL76z1dT1i4bh/z1h4goEF3Gnbur1eOpMgeGzvY7eropz+cx+/zP2fpvFkkxZ3B6bBTs0Ez+l0zlg59BmnmRUQwDJjQo16B/jAosSNddwZ3bsigTg344+ApXv/8aYyISoRWrk3rK27wdWlSClltNvqO/Bd9rr6ZzPQ0LBYLwaFhvi5LRPyU34UunJt6blm/Gp/cc+7d3l82HWT2tAcJb9aLhh0vJyAoyMcVSmljGIbbs3VFRAqqVOzFOKB9Pabf2Ytu1j2s/+DfbF26gIzUFF+XJSIicgG/HOm6M6pHE67u1ojNfx5nyqdPYClXmajqDWneb5SvS5MSLCnuNNunXrwHsj31rA+qEZHSrFSFLoDFYqFD01rMbFoLgO/X7mP+9Ico37o/9dr1xmordR9Zisiw2IgZfPEx0bFfu97eUUSksEp9Ag3p0pAhXRoya8kmfp46l+j2g2nUvscFx7BJ2VYupgLV6zS86OfZMdqURUSKV6kP3b/d2LcFY3o2Zd2u/Uyd/hOB0dWIqdOMpj11youIiHhHmQldAJvNSrdW9ejW6txRbLNX7ObnaQ9TufMQ6rS6TO9cioiIR5WK1cuFdU2Ppnx4R3fqxy1j2Zt38cfaZWRnZfq6LBERKaXK1EjXFcMwGH9lG27q14IV2zYz7f25BMVUp1L9ljS+bGD+HYjfy2+bRnfbRIaHR+hYPRG5JGU+dP8WGGCjX4fG9G3fCIDPl+7gt2nLqd7tamo37+jj6sST8gvOtLRUt9tEiohcijI9veyKYRgYhsGNfZvz8R3dqHr0Z3574y72bF5Nbna2r8sTERE/ppFuHgzDYNKQdowfkMuvm1Yz871vCK9Sh0r1W1G/Y19flyciIn5GoVsAwUEBDL6sKQM7NybX7mDWsrWsnPYrtXuNpkbj1r4uT0RE/IRC9xJYrRasVgsTrmjNeNPkjXnf8dvPH1On383UbtwSW0Cgr0sskUr7QqTS/vlEpPgodAvJMAzuH9GJjKwcflr/K1+/O4vI6g2o1LANdVt383V5JYq/L0TKb3Wzv38+EfEehW4RhQYHMrJnC4Z0bUJWTi6fLVnCqvU/Uq/fjVSt19TX5Ukx0GhVRIqLQreYBAbYCAywMWlIO5xOJy9/+wVLFqZR/4pbqFG/qQ5aEBERha4nWCwWHr6mC6npWcxf8z3zF82gfO2mVG7Qipp651dEpMxS6HpQRFgwN17emqu755Cemc3MpQtZuWYBDfrfTJXaF59qIyIipZtC1wtCgwMJDQ7k31d3wm538MLsT1m6MJvGV02kSq36WKxWX5foUfktRPJ3pf3ziUjxMUzTLP5eV7/tgU5Ll6TUDGav3MPiP9OIqdeMyg1aU71RG1+XJSIil8gwYEKPegU6pk6h62Op6VmkZGQxY8ke9iRaaHrVLVSoVtvXZYmISAEpdP1Urt3BM1+tZX+ik+ZDbqdCtVpYLNoeW0SkJFPo+rmzSWl8uXw3q47kElO/FZUbtNI7vyIiJZRCt5RITMkgMTWDGUv3cCgjmOaDb6V8pWq+Lusi+W2DqG0SRaQ0u5TQ1erlEiw6MpToyFCeurE7Wdm5/O/LKWxPt9JqxCSiK1bFMAr0f2OPy28bRG2TKCJyjkLXTwQHBfDi2B6cSUhh5pJ3WRprpUKDNlSq30Lv/IqI+AmFrp+pXD6S/4zqSmxiKgkpicxY+iErlkTQcuhEomIq+bo8ERHJg0LXT1WKjqBSdAQvjK1MRlYOT3zxGttzgmkz8m4iomJKzNSziIj8fwrdUiA0OJBXb+nJ8dhEPln8GpvSwqjYsB2V6zWjYvU6vi5PRET+otAtRWpUiua/13fnRFwSZ5OOM3PxUvYQTZvhtxERFeOx5+a3DaK2SRQROUevDJVyqelZ/PeLtSQQSftr7iVUQSciUqz0nq5c5NCpeD785Q+OOitSoWFbKtdtRkzlkvfOr4iIv1HoiluHT8UTl5TGzOX7SQ6sQrvhtxEWUc7XZYmI+C1tjiFu1akaQ52qMXRsWpuk1Awe/+IZ0gJi6DT6fgKCgnxdnohIqaaRrvDnsTimL9pBbGAtKjRqR9U6jYiqUNnXZYmI+AVNL0uh7DsWy5mEVGatPEhmeE3aD59ISJgWXomI5EWhK0V2NimNx7/cQE5YVTqPvheL1errkkRESiSFrhSbPw6dYdqiHaSWa0SFRu2pUqsekdEViv05GakpHNy1BafTSZ3GLYksX/zPEBHxBIWuFLudB09xOiGFL9ccxVG+Hu2HTiAoJLTI/WZnZTLn3RfY8Nv31GrUHKvNxqFd22jRuRejJz9BRFT5YqheRMRzijV0DcMIBpYDQZxb7TzHNM0n87xJoVtqmabJqbMpPDl7M0TVouM1dxd6n2d7bg5vPjCW6IpVuObOR8+PbjPSUvlh5tvsXL+cB9/+htCIyOL8CCIixaq4Q9cAwkzTTDMMIwBYCdxjmuZatzcpdMuETXtP8vGvO8mu1IKKDdtTuWZdwiKjCnz/6p+/ZfVP35Jjd5KennZRe0ZiLP2uvonB/9K5uyJSchXre7rmuVT++0/EgL/+UagK7RtVo32jamzee4xTCUuY89lRrFWb0W7Q2AK987ty4TcMuO5WZk9/0+Uh93vfuZUVC79W6IpIqVGgzTEMw7ACm4AGwDumaa7zaFXiV9o1qgnAwE6NOHwqgWc/fpSAyg1pP+K2PKee404epXbD5m7brYFBxJ8+gsNux2rTPi4i4v8sBbnINE2HaZptgBpAJ8MwWvzzGsMwJhqGsdEwjI3T5q8q7jrFD1gsFupVr8DHd/dlbEuDLdPvZ+MPn3F0704y0lIvuj40PJLkhDi3/ZlOBwGBQXpdSURKjUsaPpimmWQYxjLgSuCPf7RNA6YB+k5X6Nq0Bl2b1mDNzqOcjP+BeT+fIKxeW1pfcT22gEAA2ve5ipU/fOO2j6zUJDr0GVTohVoiIiVNvqFrGEZFIPevwA0BLgde8nhlUip0bV4LgGFdm/DnsVhenv4IoTWb03bwOHoNvZ5nJgzBtAZedF/Kwa1kJCcwYPR4b5csIuIxBVm93AqYAVg5Nx39jWmaT+d5k0a6kodl24/w1cr9GHW6YAZF8uHT92Kx2ggMi8QwDHIyUsnNzqJy7YY8/ekPvi5XRCRP2hxD/MLSrYc4lZDG91tOEu+MINsOpgH1mrWlQ99BBAWH+LpEEZF86Wg/8Qt92tQFYFSPpuw4dJo3f9pHuXptaD1wlI8rExHxDI10pUT5ZdNB5qw9RHDjXlRu0JqoilUIDAr2dVkiIm5peln83s8b9nMyIY3FO+OIad6Dlr2H69UhESmRFLpSamRl57Jp3wne/e0QMY070vLy0b4uSUTkAgpdKZXmr9nHws3HiGg5gEr1W1KufIXz7/yKiPiKQldKtXmr93AqMYPf/0ykctt+NOs2EIulQJuriYgUO4WulAnpmdms2XWcj1YcpUqL7jTrPdzXJYlIGaTQlTJn9oo9/LztFDEdhlC5XnPCIqN0SIKIeIVCV8ok0zT5etkuTidlsPZwGtU6XkmTzv20d7OIeJQ2x5AyyTAMxvQ5d1RgclomK/7YzmfvLKJm29407jbIx9WJiGikK2XArCU7+W1XHFUvG0mluk0JCQ3XO78iUmw0vSzyD6Zp8uni7ZxJzmTLyRxqdRlMw3bdNfUsIkWm6WWRfzAMg3EDWgOQkJLOkq1rmP3uQmp3vJyGnfr7uDoRKSs00pUy7cNF21m5L5Fava+nUp3GBAYFa/QrIpdE08sil8DpdPL+j1uIS8lid7xJ7W4jaNCqk6/LEhE/odAVKaTYxFQWbTzIgp3JNLxsIHXb9fZ1SSJSwil0RYrBez9sYd3hVOoNGEelmg2wWK2aehaRiyh0RYqJw+FkyoKNnE3J4mB6EPV6jKJO01a+LktEShCFrogHnIhL4sf1B1i0P5MmPYdRu2UXX5ckIiWAQlfEw96Yt5GtJ7JoNOhWKtWo5+tyRMSH9J6uiIfdN7wDdruDV779mD9/y+aUPZIGva+hRv0mvi5NREowjXRFisGR0wnMX7uPVcehSa9h1Gja3tcliYiXaHpZxEdy7Q5en7eJXbF2mg65nZiqNX1dkoh4mKaXRXwkwGbloVGdyMm18+Ls9/gz084ZytOoz7VUqaXvfkXKOo10RTxs3/E45q3Zz+b4IBr3HEr1Rq19XZKIFCNNL4uUQJnZObz63Sb2Jhm0HD6J6IpVfF2SiBQDTS+LlEAhQYH897quZGXn8vzstzid7STBVolGfa6hYrVavi5PRLxAI10RH9p5+Azfrd7HnswomvQaTpU6jX1dkohcIo10RfxE8zqVaV6nMslpmbwybwYrfg2g9dV3E1m+gq9LExEP0EhXpARJz8zm2a/XkeWA5ODqNOlzDdGVqvq6LBHJgxZSiZQCW/ad5NvV+zhmVKVh9yFUrtXA1yWJiAuaXhYpBdo2rEbbhtWIS0zllXnT2ZsbRtuRdxNeLtrXpYlIIWmkK+InUtIzeebr9eSaFtIj6tKk90h99ytSAmh6WaSUW7f7OHNW7eVsWAMadR9Ehap65UjEVzS9LFLKdW5ag85Na3A8NpHXFkxlt1mO9qPuJjQi0teliUgeNNIVKQUSUzJ4+psN2LGSG9OIJr2vJiyinK/LEikTNL0sUob9vuMo367aS0aF5jS6bCDRlar7uiSRUk2hKyIcOB7H6wt3kBZUiQ4j7yI4NMzXJYmUSgpdETkvLjGVZ+ZswjQCcFZpTpOewwgODfd1WSKlhkJXRFxavPkg367eh1m7Cw079adcTEVflyTi9xS6IuKWaZrsPnyat37cSXZ4dTqMnERQcIivyxLxWwpdESmQU2eTeWbOFgxbINaa7WjSfTABQUG+LkvEryh0ReSSLVy3j3lrDxDYqBcNOvYhIirG1yWJ+AWFrogUimmabN13nLd/3oMZXYeOI+8gIFAjX5G8KHRFpMiOnE7ghbnbMWyBBNfvQuPLBmK1aRM7kX9S6IpIsZqzcg/frz9IeOuBNGzXQ7tdifwfxRq6hmHUBGYCVQAnMM00zbfyvEmhK1LqOJ1O1u86wru/7sNWqREdr56IzRbg67JEfK64Q7cqUNU0zc2GYUQAm4DhpmnucnuTQlekVNt/PI6XFuzEGhBERNOeNOzcH8Mo0J85IqWOR6eXDcOYD0w1TXOx24sUuiJlxmdL/mDR5qOU7zScBq27EBIW4euSRLzKY6FrGEYdYDnQwjTNFLcXKnRFyhSHw8nK7QeYtvQQITVb0HHoLVisVl+XJeIVHgldwzDCgd+B50zT/M5F+0RgIsAHD45uP3FYt4JXLGVaXGIqHy1cy5JNe3E4Tbo0r8Ntwy6jVpXyvi5NLpFpmuw6EsvrP+zGGhhE+db9qd+ul6/LEvGoYg9dwzACgIXAItM0X8/3Bo10pYB+Wbeb65+aydDuLRjZuw0BVgs/rNnFrEUbeGPy1dw8sJOvS5Qi+HDRNpbsOEmVbqOp16I9QSGhvi5JpNhdSuha8u/MMC4ACQ4AACAASURBVICPgN0FClwptc4mpTHy4feJT04vlv4OHI/jhqdnMu+FCXz86A10blabD+b+zhPjrmTV+/fx8HsLWL3jYLE8S3xjwhWtmXnvAPpb1rNy2iNsWvgpTqfT12WJ+Ey+oQt0A24C+hqGsfWvf67ycF1SAs38YTWJp48xY+GqYunvne9WMGFwV7q3rn9R/01qV+a/467g9a+WFsuzxHdsNisDOzfh83sv59ZmOayd/jDrPnqcIzvW+ro0Ea/LN3RN01xpmqZhmmYr0zTb/PXPj94oTkqOs0lpLPx9A+9dXYGFv28oltHugpV/cMMVHdz2f8OADixY+Qce2cBFvM5qtdC+UXU+mdSL6RO7UO3EYpa+NZl929aRm53t6/JEvKIgI10RZv6wmsENLDSuFMTgBpZiGe1mZudSLizEbf8RocE4nE4cDk1HljaGYXDn4HZ8cndfLstczu/vP8TWX77SX7Ck1FPoSr7+HoXe3C4MgJvbhRXLaLdFvaos37rfbf8/rN5Jo5qVsNn06klpFRQYwPDuzf5fe/cdHlW1NXD4tzMtPaTQElrokNCbCFJFkaKIV0ABFfDSlOsVFPjUq1wUrNcCCohYQKJIkaogCtKLIL1DEkJCSUghvc3M+f4IIEgCCcnMZJL1Pg8PJMzZZ80hYWXvs87aLHyhG0/USGT755PY9eV/iD6+z9GhCWETknTFHV2bhQZ45jW7D/DUl8hsd3S/Drz/3Ua+XLn1lvF71VG8NHM5o/vJo2flgUGvo2NoTeY/35l5I9vhe2Y1v8/4NxHHDmDOzXF0eEKUGNnwQNzRwxM+5UJc/C2fD6wUwKr/PX/X41qtVp56cyFrdx6lkrcBD1Ne0s3KsXA+MQO9Xk/MyrcwGaW/b3mUmZ3Dih0nWL7/MpVD76NJ137SalKUSrLLkHAaFouVjxdvYubSLUDejCc1I4t/9r2XV59+AFeTJNzyLis7l21Hz7Fgx3l0BhO1Oj1OYL0mjg5LiOsk6QqnY7FYibgQj9WqERzoj9Eg+7aKW1mtVt7/cQ8HY9Ko2/OfBNaqJ3v8CoeTpCvEDS7GJ/PFqh1sOxSBUoouLeoyom97KvlKY35nlZaRzdKtx/n52BWqtexGow4POTokUY5J0hXiqiUb9zP6/R8Y0K0FD3dsglXTWL75ICu2Hmb+a0PofW+Io0MUxZCWkc3Gg2dZuu8yLgYTdbs/QeWaDRwdlihnSrQNpBD2UtJtJvedjOb5D5eyccY4Zr88kNqB/jz/XhiThtzPT++PYti0ME5ExZbIuaDk4xd35ulu4uH2DVjwXEe+HNEK9nzLps9e5mJUOFaLxdHhCXELSbqi1CjpNpMfL97EpCHdaVYvCIDJny3FT5/JxJlLaBdSi7H9OzJz6eYSOReUfPyiaHQ6F14b1J5ZI9pR6XgYG+e8yqk9Gx0dlhA3kaQrSgVbtJlcufUwQx5sA8DJqFgOnwjn634eHD4RzunoOIY82IaVWw8X+zxgm/jF3fHxdGN0n1bMHdGaNjl72fT5K2z76r/ExcjmGcLxJOkKAMxmC3+eOMeOwxEOSRi2aDOZkZWDj4crkDfLfTJUT9MqBp4M1TNx5hIqeLqRmZ1b7POAbeIXxePj6caAziGEPdeBL4e3IHvbPDbNnkz8xWjZ6Ug4jCTdcs5qtfLBdxuo9Y8pPP1WGONnLKfOgKkMnbqASwkpdonBlm0mtx6MuD7LHdM2LwGPaevK4RPhLNqwj5DgqqU2flFy9HodU4d0YObTLXHdM4/fP/8PZ/Zvc3RYohySB9zKMU3TGPvBEg5HXODnD0bTtG7evc/ElHTeC9tAxzEfs+PzF23+aM3t2kyOH/zAXY87ul9Hpi9Yj7er4slQPVU983o4V/XUMbCxjtfnruHTCQNKbfyi5Pl5e/Bi/7YkJKfz0x9b+OXzteiMrjTu+Qz+Vas7OjxRDsgjQ+XYjsMRDJn6LQe/mYSXhyvxV9IY9c5C5v7fUPx9PHjh42VYLFY+nfA4kHdftOcLn7B+5r+pV71Skc+363AkvcbPYN0nL9C2ca3rn7/WZtJssXIxMZ2qfh7odS7FbjOZa7bQd+LnbNl/Gk+DhkGXV9Gfa9FIybLiotOR+tv/0OmKt+BjqzaZwj5ycs28HraTmAw9zfqPw8e/krSbFEUiz+mKQnn6zYU0qxfI+EHdAPgwbD1rft1Mnx6dGT/4AaJjk2j29LucXzkVN5ORRyd+xrmIM9SoXZfl7z1X5PO1GDoVlZGI5u7H/m9fv+Xv/37+kpCdk8v0Bb8yd9UOvNxNWCxWsnPNjH30PiYO7i47GInr4pJS+erXIxyM11H73j7UCm3j6JCEkyhK0pXl5XLs5LlYRj+at4vPjdW3Y9bs4ek+Hahe2RcvdxOXElLJyTVz+EQ4Pw7woP/ivOrfosx2dx2OJCkhgRWD3Om3KIE/jp29abab3/n9fTyK/R5NRgP/fbYXrz79AOHn41FKUTcoQJKtuEUlXy8mD2jPpYQUVu1ex+87VmEwuRHSazi+Fas4OjxRRkghVTnm6W4i/koakH/1ba7ZQnJ6Fp5uxnyrf4tizHvfMripgeZV9AxuamDU2wuu/11iSjrDp33LpfgrvLAiDoM5nRmLfiv02HFJqby9YD29JszhoQmzmT5/PbGJfxWBXUpI4b2wDYyfuZzxM5bzv0UbuZyUWqT4RflRxd+bkb1a8t3Ye5gzpBFxaz9i65dvkJ5yBZusDIpyRZJuOfZYl2bMX/tHgdW3C3/ZQ9M6gSSmZORb/Xs6Oq5Q57k2y32ujRGA59oYSUrIm+1u2HuS+gPf5I+jkUzu6su4jr4EebswPWwjn6+482M3q7YeptGT0wg/H8+YRzsw9tGORFyIp/Hg6azceohlvx+g8eDpnItN4rn+9zG6XwdORMXR8MlprN157C6vnCgv3ExGPhjeiXf6NyDp53fY/PU0zp085OiwhBOTe7rlyN8LpVLTswgd+jbtQ2rQ1iuO8Z18rr/2lbUJzN6VStgbT/PFik000Ucxtdtfy72vb0znsLnmTfd2CyqUajF0Kr2CUnirmyualnf/47WNWSw/68Hl1BwGdG5KHc4yso0bp6NjqV+9Mv/dkMKc3ams/2gs94QGA7cWch2NuEjXcTP5+YPRtG5U46b3F3khgQdenAVobJwxjub1q910fHxyOg9PmsvWWf+mYc3KNrm+ouyJiUvixx2n2HkBDK7uNOvzLN5+AY4OSziYFFKJfOVXqHT87CVaD38fnYuGn6cJnVKkZOaSlJ5D45oVObTwNWo9MpncnOxbxjMYTZxd+c71jwsqlAro8SJ6dWszguRsGDegGyciorkQF8/F+GTQrKBcqBrgQ7ZVT6PagSx+czjALYVcI99dRM0qvrz69IP5vr+Wz7yHwaBj9xcT8j3+jXk/k5Ccfr062xbXV5Rd6ZnZvLJwJ0l403bQBExu7o4OSTiIFFKJWxRUqNSoVhUS173Dj5sPsXr7EbJzzDSvF8SIPu0JrJg3870xsRbkdoVS8b9+RPyVNAZM/ITZfdwZsyaDJe//m3tGfsjQnm1oUucRdh2OZNCkj1nxhDv9FmUw6/+eoUGNylTq8wqapnHqXNwthVwrthxi75cvFfj+TsdcxmjIK5i61iDjxuOH9mxDl+dnlEjStVUhmCi9PNxMfPLPLkRdSmT20jeIsQZQp2M/qtWRXY5EweSebjlxuzaFJqOBJ3q04rspT7Ns+gj+M6zn9YRbWLcrlCro/BlZOVTwdANg1LsL6BasI9sMj4foGfX2Arw9XDFbrFgsVl6euZguNVzIMsM/GumYOHMJGdk5VPByK3D8zOwcsnPMwJ3bQKZlZPPHsSj2Hj9Hdk7RW0NKG8jyq2YVP94Z1oWpPavgcfBbNsx9g63fvk9acpKjQxOlkMx0y4Frs7DFA/I6Sz3V0oMBi0tuNna9UKpf3ljPtTESduiv2W5B569fvSKb9p1m477THDmbQK6/C4diswhPtGDR4vl06WbqBAUw9n9L+GnXaRpV1LH/UjqRSVasnCI4sArbDkbQtnHNfMevVqkCbibD9VnurOGeQF4h2H1fhfPDhn00rFGZf320lIW/7CU40A+LReNCfDKjHunAf4Y9iNFw528RW19f4RxqBwUwcUDe/d2U9ExeWTiNNIM/7Z58Cb3e4ODoRGmhmzJlSsmPGv2HDQYVd2vOsk3UN1yie728e07uRhcSUnM4eMlM+6Z1ij1+35dm8lg9M/0a5lUne5lcuJxh5aOfTzC6f+cCz59pDODjxZs5FnmBZ5rqWDvEk5GtjAxpauSn07l8u/E4/l6eHA6P4akmsGawD6Nau/JEEyNrTuUQGZ/Fqeg4srIyaWiMvWn8uORsNoVn4u5qYv+JcO6vmsrDDUx58RldiE018+6aU2jkPZ+5dNowXn7yfkY/2pF+nZowf90fLNl4gAHdWuDicvtbNba+vsL5mIwGerUOJrSiC+uWhnHyxHEM3hXxquDn6NCEDSgFLWv6/rdQr5VCKudxt9Wx19oUZuWYib6cSvWKXrga9UVuU1hQG8iCCqXMmgvxv350/fw5uRai41OoHuCN0aBDczFyNi6F5NQMfExcb9NotmqkZkOOFWpU9gVzFubcHNDAYrWic3FBQyMpC2pXq0RCUir+nnrcjHn3bzNzLJxPzMBoMNC0bjV2HA7Hy6Ch/1sbSAuKh+4JYeW7/0QpddP19fZw5b4xH/PiwK4MvL9loa7v30kbSHHNyahYFm8/zbFUD0zuHrTo+yzunrbtaS7sR6qXy6jiVscWt43jnY6/U3x/P77buJmMfPheomIT+XTZVpJS0rGYc7EqHeMHdmX19qMkpKRzcdVb+Y6/5cAZxry/mAHdWzBnxXa83V1RCpLTsxj1yL288tQDaJrGW9+s54vVO/D1csditZKemcOYRzsy/+fdLHh9KO2vPpL09/GX/r6fWT9uY+PMcUW+VkLkx2y2cCUtk1e/30OuRxXaDXwRF510R3N2RUm6UkjlJIq7SXp+m7iX5PF3ii+/40+ei6Njs9pMGtKDdf8bg5/JysL+HgR5wfA+93AxIZm4pFTMZku+47cPDebEuVheH9aTqGVT+HH6CJZNG0HUsilMGdELo0GPyWjgzZG9iVo2hSVvDWP59Gc5u2wK/ze0B+EXEmjXuGaB8bcPDeZ4VGyRrpMQt6PX6wio4MnnY7ryUmc/jn7zMjuWzCL+YrSjQxN2IknXCWTn5PLCR4uJT0zmhRVxWDLTeG/BuiKNUdw2jnc6/nbVu8lpmTz+6he4aFYm/5ZJkKfGuA++w8PNSEJyBgCvzfmRZ5obeCzElSFNDEycuQR3kwG9zgWdziXf8ROS0/F0M6GUwmjQE1K7KiG1q95S/BSXlMqHi35n0qxVTJq9ik8WbyIpNQOTQU9SamaB8V8bXwhbaFyzMjNGdefFtgbSf5/J5vnvsmvpbLIyZB/mskySbikXHnOZRoOns3bnMca09+GF+3xpVc3Ix0u3MmHG8kL1gi1oE/fCznbvdPztNnHfdjCcOo9PJfL8ZSZ2dGVsG1eCfXX8+mcEQQE+fPPz7gLHr+rvQ52gABKS0/Mdf/bybTzWpdltY1+++eD1NpGjHunAs33bczj8Ig2fnEb70FrMX7u7wPjnrNh+x/GFKK6mdQJ566nOfDGkPlO7e3Pw29fZvfgTrNZb6ySE85Pq5VIsJ9dMhzEf07R2FYaEwIP13Xji2/PMeLQKvu56vtl2Dh9PN1o1yNt8O/5KGkOnfMX9bRrh7mq8Ps4/p8/n/io3V+/Gp1sI23WeQT3aXn/d3R5fUPXu5jOpvDxrNfWDfHmyQQ7/au+GwcXKU83duJxuYfXBBI5GXmL7wVM8Epx10/iHL+Wy/vgVsnLMxCclc49fyk3j7z+Xxqz1J/li0hNU9vPO9/odOBXDoDfm88tHYxnVryP+Ph5M+/onPn1pID3aNODDRRvZfiiSlLR0Wvsk0ayqkaFh53k41JsD0WmEbYvk61cHX3+WWAhbMuh1eHu48nDbYGq4ZbNuWRhRUedw96+Km4cUXZVmUr1cRvzw2z4+X7kdT6PiQlw85+NTqOpu5WKGC0EB3ri6eRCbnMXJ71/FxcWlwEKmwrZxvNvjC6reTcmGnu1DWbVpL7k52VitGi5Kw6opXFwUWVYdjWtXY8fhCIw6cDUoFIoss0ZWrkaAl5Efpo2k5/jZuJtc8PXI+0EgOSOX5IxcWtWryu6vJhd4/Z55ayEhwVV5eXD3fN/f61/8xJ8novl1zwm83PTolIa7zszlDEWORaNtgyC2fzGxEP9SQtjGnhMx/LDtFPGmahjdvWnWczBGk6ujwxJ/I9XLZcSg17/hwXYNGdb7Hk5GxdJ73Lv8OMCd/oszWPvZZOpWq0i9gW+y/O1nqervfUubxaI8VpRfm8biNnYIHfI2X7/6JG0a1cx3fFejHt+ek4ld/RaLNuxj45+nsVo17gmpxbDe7QiokNfMIiU9kwVr97Bp/2k0DTo0DeaZXu3w8759fD4PTOTk969Rxd873/MnpqTTddxMDs6fzKdLt/DZko20DNQRmaLjl5kTqFVVnqkUpcOV1Azik9OZtvwQhkp1ad1/DEoV6v94YQdSvVxGZGTl4OuVt6SaXyGTUgpfL3cys3OL3YbQFm0MM7L/iv+rVdtoHmAhJctKj1owf812PNxM6FxcMBkNjHn0Ppa8NZxl00fw8uDu1xMugLeHG8//oxNLp41g2fQRjB/U7Y4JF65dv4LbRF67dv4+Hni56pjczY91I2swqq0nP27cW+z3f6PktEz2HI/iwKkYzGZLiY4tyr4KXu7UrVaRr8d159nmBvbPm8CfaxaQknjrCpMo3aQNZCkWElyFbYciaFSzcr5tDPcej+J0zGUqeLoVqw2hrdoYhgRXYfP+M3y5Zicffr+R2n56Np1JJTIxFw+3DdSrWZXKfl64mWzTIi8kuCrbDkXQrG5Qvu+vol8FGtWqYtM2jokp6UyevZolv++ndqA/GVm5pGVm88LjnRk/qCsuLvJzryiatg0CadsgkG1HoliyfDppPvUwuHvR9P7HMRil2r60k0KqUqx2oD/PfbiEw6fP0jMo/ZZCpjeXH6Nzi3qkpaUXqw2hrdoYerqaGPPBD5y9mMCwlu6sHB7EqPa+DG3lzc9HU5i1eh/PPdaJri3r3fU5bkdD44tVO0lLS6eB8dItbSLfXX2MiUPuZ8fB0zZ5/0kpGXQaO4O61Sryw9RhTHiiG88/1onurerz0Q+/s/NIJH07hsoyobgrNSpVoGerYNpX0xHqncEP3y8i4dI5qtRvKV9TdiaFVGXI2wvW8/q8n/AxKYw6UEphsWqk52hkmuHssimMfTesWG0IbdXGcOWWQzwzLQwXBZW9DbgadHnPvrq7cik5i9RMM5OH9mDqP3vf9TluJyfXTK+X5nA04gL+HjpcDX+1iYxJyMDN1UjMirfoP2m2Td7/pFkruXwlja9eGQzc3MbTZNDTcvh7fP7yQLq2qn/X5xDiRluOnOO7rWfQ1WxH3XY98PTxdXRI5YIUUpUxC3/Zwzvf/kZyeiZ+3u5EXUri4Y6hTB/Vh2qVSu83Ve+X5vB41+bEXUlj5tItZGXnkpmZiaZ0jOrXgce6NOOJKQuIWjbFZj+ZZ2XnMvXrdcxbvZOKFTyxWK0kp2cxul8HXnnqAQx627TgM5stBD7yH7bP+atP9d+rpz9btoWtByNYNPUZm8Qgyq8N+yNZuuM0lqrN0Lt60aRbP9npyIZkE/syZsiDbRj8QGtOR18mIyuHmlX88PV2d3RYd3Q8Kpb7mtWhTrWKPNWzDY+8+DGvdPLhw11mXn36Qfx9PLiclEZmdu5NzwWXJFeTgemj+/L6sJ6cio5DKUWDGpUKtWVfccQnp6OUup5w89vk/t4mwXyxaqdN4xDlU/cWwXRrXouYuCvEJsXz0ZxJ+NZuTrNeTzk6tHJPkq6TUEpRv0alO7+wFPFwNZKQkkEd4Lt1uxkYauSRUG/CE5OZv2Y7Ix/tjFWzYrTRbPNGriYDTesG2fw817i7GkjPyiY7JxeT0fC36uks5q/ZTrMGNXF3ldmHsA2lFNUr+1K9si9hDWvw2/5IFs+dgKn+fdRr010abjiIJF1hM/07N2PB2j+oHeifb3WwVenp3T4EvR2Srr15e7hxT0gtlm06yANtG+b7/nefOM9jXZo7OFJRXtzfIpj7WwTz0+7TrFjwKvpa7TC4eRJyXx/Z6ciOJOmWIne7X25pNeqRDrQY9h7Z2Vn0qetCgGfel1uAp57WlSxM/Wot6z4a4+AobWfS4PsZNj2MI2eib3n/1d2zWbHrGLNfHuDgKEV507tdPR5qU4eICwlcTIxm1qyXqdiwLU16DHJ0aOWCFFKVIsXdL7c02nkkkq7Pz8DN4IKvpxGlFMkZuVxJz6FZ7cr8Of8VR4doU/NW7WDsB4up4GHAy82A1aqRlJ5DRraZ9o2rs3nOS44OUQh+2n2GFXuj8QrtTu2WnTG5lf6akdJEqpedkC3aMJYWaRnZhK3fy/o/TmC2WGjXuBYj+t5T4EYFZc35y1eYt3on+05GY9Dr6H1vCAO7t7RZ8ZgQd+vHbSdYtfcs7g07Y3TzolH7HtLApRAk6TqhD8PWw/k/Gd/Jhw+3JENQqzIz2xVCOA+LxcqJqFguJKbx5dZoqoR2IKRrf0eHVapJ72Unc7v9aIUQwp50OhdCalelR+t6LHqxG53cz7J77suc3rsp393GRNFI0i0Frj1OcmOhTUltOiCEEMXRv0MD5o3tQrOM3eycM4FDm1dzYs/v2GSVtBy44/KyUuoroA8Qp2laaKFGleXlIrFVG8a/K2vV0UII+8o1WzgScYGYhHQW7LxIjZZdaNixj6PDcrgSvaerlOoEpAELJOk6t7JYHS2EcJzvNh3jt6NxVL6nPzVD2qDTl8+nUEv0nq6maVuAxGJHJRzqxjaEcr9YCFESnuzSmC/Hdqbu5Y1s+fRFjmz/lVMHdsrS822U2D1dpdRIpdRepdTeuSvlXmRpY4tN6oUQQinFiJ7N+fpf3XnC/yRt0zazfuZETu76xdGhlUqFemRIKVULWCPLy87p2jPAiwd4EeCpJz7NzIDFqWXqWWAhROny9a+H2XLqCtU7DSSoQbMy/byvPDIkbiLV0UIIexvWowlfje1I5bOr2TTjBY7+sZnwI/scHZbDyUy3HLBXdbQQQuQnMzuHPcfPcfZyKiuPpFD/3oeo3aqro8MqMSVdvfw90AUIAGKBNzRN+/K2B0nSFUIIUYA5Px9gV2QqwT2eIrB2Y0eHU2zSBlIIIUSpZrVa+XjFHnZHJFO9y5O4uXtSo36Io8O6K5J0hRBCOIW0jGy2HzlL1OVUfg3Ppn7HvtRq2t7RYRWJJF0hhBBOx2y2MOvng/wZk0ndB4dRpWY9R4dUKEVJuuWzfYgQQohSR6/X8a+HW2KxWHlv2Xw2rEwnuPtTuLp7EBhc39HhlQiZ6QohhCiVktMy2XzoLGfjUth+wYUGHftQvXFrR4d1C1leFkIIUaZkZucwc/UBjly20LDXs1QMquXokK6T5WUhhBBlipvJyMR/tCXXbOHtJXP5PS6X2j2exs3dk0rVajk6vEKTma4QQgink5iSzoYDZ4m8lMKBZHfqd+hNUP1mDolFlpeFEEKUGynpmcxYfYDTyS406j0K/ypBdj2/LC8LIYQoN7w93HhtUHuyc3J5a/GnHErSaNBzOCZ3T/wrBzo6vJvITFcIIUSZEpeUyvo/IwmPTeZMjj/1OvalanBDm51PlpeFEEII8rY2/WTNAaLSjTTp9xwV/CuV+Dkk6QohhBA3yMjKYeqi3cRk6GnU61mMbu4lloAl6QohhBD5uHA5mbV/RhJ+6QoX9NWpe28vKteoW6wxpZBKCCGEyEdgRR9G9GwO5CXgT376glO5njR/dCxeFfxtfn6Z6QohhCjX0jKy+e+i3VzKdSO070iMJjc8fXwLfbwsLwshhBBFFHUpkTV7IomMTSbRsx717u1JQGCtOx4ny8tCCCFEEdWs4sdzff0AOHsxgRk/fcYJ5UuLR8fi4V2hRM4hM10hhBCiAFdSM5iy6A8SqUCzR0ZhMJlw8/C66TWyvCyEEEKUoNMx8azeE0HkpWSyKjenbrse+FXOazcpy8tCCCFECapXLYDx1QIAOBkVy8zVH3LMVIlW/cfi7ul1h6P/IjNdIYQQ4i4kJKczZdEe0k0BfDX3c5npCiGEELbi7+PBzFFdCL94pdDHuNgwHiGEEKLMqxPoV+jXStIVQggh7ESSrhBCCGEnknSFEEIIO5GkK4QQQtiJJF0hhBDCTiTpCiGEEHYiSVcIIYSwE0m6QgghhJ1I0hVCCCHsRJKuEEIIYSeSdIUQQgg7kaQrhBBC2IkkXSGEEMJOJOkKIYQQdiJJVwghhLATSbpCCCGEnUjSFUIIIexEkq4QQghhJ5J0hRBCCDuRpCuEEELYiSRdIYQQwk4k6QohhBB2IklXCCGEsJNCJV2lVE+l1Eml1Bml1GRbByWEEEKURXdMukopHfAZ8BDQGHhCKdXY1oEJIYQQZY2+EK9pC5zRNC0CQCm1CHgEOFbgER6VSiQ4IYQQotRThb9TW5ikGwRE3/BxDNDulnMqNRIYefXDUZqmzS10FE5EKTWyrL43e5DrVzxy/YpHrt/dk2tXMgqTnlU+n9Nu+YSmzdU0rfXVX2X5H2bknV8ibkOuX/HI9SseuX53T65dCShM0o0Bqt/wcTXggm3CEUIIIcquwiTdPUA9pVSwUsoIDAJW2TYsIYQQouy54z1dTdPMSqnngV8AHfCVpmlHbR5Z6VWWl87tQa5f8cj1Kx65fndPrl0JUJp2y+1ZIYQQQtiAdKQSQggh7ESSrhBCCGEnknSLSCmlU0rtV0qtcXQszkYpdVYpdVgpdUAptdfRkxf7/wAAAnFJREFU8TgTpVQFpdRSpdQJpdRxpVR7R8fkLJRSDa5+zV37laKU+rej43ImSqkXlVJHlVJHlFLfK6VcHR2Ts5J7ukWklBoPtAa8NU3r4+h4nIlS6izQWtO0eEfH4myUUvOBrZqmzbv6FIG7pmlXHB2Xs7na1vY80E7TtChHx+MMlFJBwDagsaZpmUqpxcDPmqZ949jInJPMdItAKVUN6A3Mc3QsovxQSnkDnYAvATRNy5GEe9e6A+GScItMD7gppfSAO9Kr4a5J0i2aj4GJgNXRgTgpDVivlPrzattQUTi1gcvA11dvbcxTSnk4OignNQj43tFBOBNN084DHwDngItAsqZp6x0blfOSpFtISqk+QJymaX86OhYn1kHTtJbk7Vj1nFKqk6MDchJ6oCUwW9O0FkA6IFtsFtHVZfmHgSWOjsWZKKV8ydvkJhgIBDyUUkMcG5XzkqRbeB2Ah6/el1wEdFNKLXRsSM5F07QLV3+PA5aTt4OVuLMYIEbTtN1XP15KXhIWRfMQsE/TtFhHB+Jk7gciNU27rGlaLvAjcK+DY3JaknQLSdO0/9M0rZqmabXIW6LaqGma/LRXSEopD6WU17U/Aw8ARxwblXPQNO0SEK2UanD1U9253daaoiBPIEvLd+MccI9Syl0ppcj7+jvu4JicVmG29hOiJFQGlud9z6IHvtM0bZ1jQ3Iq44Cwq0ukEcAwB8fjVJRS7kAPYJSjY3E2mqbtVkotBfYBZmA/0hLyrskjQ0IIIYSdyPKyEEIIYSeSdIUQQgg7kaQrhBBC2IkkXSGEEMJOJOkKIYQQdiJJVwghhLATSbpCCCGEnfw/cQE2mbd8X5wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = np.where(df.Species == \"Iris-setosa\", 1, 0)\n",
    "X = df[features].values\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y\n",
    "                    , test_size = 0.3, random_state = 1)\n",
    "\n",
    "pipe = pipeline.Pipeline([\n",
    "    #(\"poly\", preprocessing.PolynomialFeatures(degree=2\n",
    "    #                                , include_bias=False)),\n",
    "    (\"scaler\", preprocessing.StandardScaler()),\n",
    "    (\"est\", linear_model.LogisticRegression(random_state = 1, solver=\"lbfgs\"))\n",
    "])\n",
    "\n",
    "pipe.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = pipe.predict(X_train)\n",
    "y_test_pred = pipe.predict(X_test)\n",
    "y_test_prob = pipe.predict_proba(X_test)[:,1]\n",
    "\n",
    "print(\"accuracy:\", metrics.accuracy_score(y_test, y_test_pred))\n",
    "print(\"precision:\", metrics.precision_score(y_test, y_test_pred))\n",
    "print(\"recall:\", metrics.recall_score(y_test, y_test_pred))\n",
    "print(\"f1_score:\", metrics.f1_score(y_test, y_test_pred))\n",
    "\n",
    "plt.figure(figsize = (8,8))\n",
    "plot_decision_regions(X, y, pipe, X_highlight=X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.9777777777777777\n",
      "precision: 0.9285714285714286\n",
      "recall: 1.0\n",
      "f1_score: 0.962962962962963\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1eb175c0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = np.where(df.Species == \"Iris-virginica\", 1, 0)\n",
    "X = df[features].values\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y\n",
    "                    , test_size = 0.3, random_state = 1)\n",
    "\n",
    "pipe = pipeline.Pipeline([\n",
    "    #(\"poly\", preprocessing.PolynomialFeatures(degree=2\n",
    "    #                                , include_bias=False)),\n",
    "    (\"scaler\", preprocessing.StandardScaler()),\n",
    "    (\"est\", linear_model.LogisticRegression(random_state = 1, solver=\"lbfgs\"))\n",
    "])\n",
    "\n",
    "pipe.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = pipe.predict(X_train)\n",
    "y_test_pred = pipe.predict(X_test)\n",
    "y_test_prob = pipe.predict_proba(X_test)[:,1]\n",
    "\n",
    "print(\"accuracy:\", metrics.accuracy_score(y_test, y_test_pred))\n",
    "print(\"precision:\", metrics.precision_score(y_test, y_test_pred))\n",
    "print(\"recall:\", metrics.recall_score(y_test, y_test_pred))\n",
    "print(\"f1_score:\", metrics.f1_score(y_test, y_test_pred))\n",
    "\n",
    "plt.figure(figsize = (8,8))\n",
    "plot_decision_regions(X, y, pipe, X_highlight=X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.6\n",
      "precision: 0.0\n",
      "recall: 0.0\n",
      "f1_score: 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10aa0dd68>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = np.where(df.Species == \"Iris-versicolor\", 1, 0)\n",
    "X = df[features].values\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y\n",
    "                    , test_size = 0.3, random_state = 1)\n",
    "\n",
    "pipe = pipeline.Pipeline([\n",
    "    #(\"poly\", preprocessing.PolynomialFeatures(degree=2\n",
    "    #                                , include_bias=False)),\n",
    "    (\"scaler\", preprocessing.StandardScaler()),\n",
    "    (\"est\", linear_model.LogisticRegression(random_state = 1, solver=\"lbfgs\"))\n",
    "])\n",
    "\n",
    "pipe.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = pipe.predict(X_train)\n",
    "y_test_pred = pipe.predict(X_test)\n",
    "y_test_prob = pipe.predict_proba(X_test)[:,1]\n",
    "\n",
    "print(\"accuracy:\", metrics.accuracy_score(y_test, y_test_pred))\n",
    "print(\"precision:\", metrics.precision_score(y_test, y_test_pred))\n",
    "print(\"recall:\", metrics.recall_score(y_test, y_test_pred))\n",
    "print(\"f1_score:\", metrics.f1_score(y_test, y_test_pred))\n",
    "\n",
    "plt.figure(figsize = (8,8))\n",
    "plot_decision_regions(X, y, pipe, X_highlight=X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.9333333333333333\n",
      "precision: 0.9411764705882353\n",
      "recall: 0.8888888888888888\n",
      "f1_score: 0.9142857142857143\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10aa0d550>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = np.where(df.Species == \"Iris-versicolor\", 1, 0)\n",
    "X = df[features].values\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y\n",
    "                    , test_size = 0.3, random_state = 1)\n",
    "\n",
    "pipe = pipeline.Pipeline([\n",
    "    (\"poly\", preprocessing.PolynomialFeatures(degree=4\n",
    "                                    , include_bias=False)),\n",
    "    (\"scaler\", preprocessing.StandardScaler()),\n",
    "    (\"est\", linear_model.LogisticRegression(random_state = 1, solver=\"lbfgs\"))\n",
    "])\n",
    "\n",
    "pipe.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = pipe.predict(X_train)\n",
    "y_test_pred = pipe.predict(X_test)\n",
    "y_test_prob = pipe.predict_proba(X_test)[:,1]\n",
    "\n",
    "print(\"accuracy:\", metrics.accuracy_score(y_test, y_test_pred))\n",
    "print(\"precision:\", metrics.precision_score(y_test, y_test_pred))\n",
    "print(\"recall:\", metrics.recall_score(y_test, y_test_pred))\n",
    "print(\"f1_score:\", metrics.f1_score(y_test, y_test_pred))\n",
    "\n",
    "plt.figure(figsize = (8,8))\n",
    "plot_decision_regions(X, y, pipe, X_highlight=X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.9777777777777777\n",
      "precision: 1.0\n",
      "recall: 0.9444444444444444\n",
      "f1_score: 0.9714285714285714\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10aa75588>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = np.where(df.Species == \"Iris-versicolor\", 1, 0)\n",
    "X = df[features].values\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y\n",
    "                    , test_size = 0.3, random_state = 1)\n",
    "\n",
    "pipe = pipeline.Pipeline([\n",
    "    #(\"poly\", preprocessing.PolynomialFeatures(degree=4\n",
    "    #                                , include_bias=False)),\n",
    "    #(\"scaler\", preprocessing.StandardScaler()),\n",
    "    (\"est\", tree.DecisionTreeClassifier(random_state = 1, max_depth=3))\n",
    "])\n",
    "\n",
    "pipe.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = pipe.predict(X_train)\n",
    "y_test_pred = pipe.predict(X_test)\n",
    "y_test_prob = pipe.predict_proba(X_test)[:,1]\n",
    "\n",
    "print(\"accuracy:\", metrics.accuracy_score(y_test, y_test_pred))\n",
    "print(\"precision:\", metrics.precision_score(y_test, y_test_pred))\n",
    "print(\"recall:\", metrics.recall_score(y_test, y_test_pred))\n",
    "print(\"f1_score:\", metrics.f1_score(y_test, y_test_pred))\n",
    "\n",
    "plt.figure(figsize = (8,8))\n",
    "plot_decision_regions(X, y, pipe, X_highlight=X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.9777777777777777\n",
      "precision: 1.0\n",
      "recall: 0.9444444444444444\n",
      "f1_score: 0.9714285714285714\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a20006470>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = np.where(df.Species == \"Iris-versicolor\", 1, 0)\n",
    "X = df[features].values\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y\n",
    "                    , test_size = 0.3, random_state = 1)\n",
    "\n",
    "pipe = pipeline.Pipeline([\n",
    "    #(\"poly\", preprocessing.PolynomialFeatures(degree=4\n",
    "    #                                , include_bias=False)),\n",
    "    #(\"scaler\", preprocessing.StandardScaler()),\n",
    "    (\"est\", ensemble.RandomForestClassifier(random_state = 1, max_depth=3, n_estimators=20))\n",
    "])\n",
    "\n",
    "pipe.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = pipe.predict(X_train)\n",
    "y_test_pred = pipe.predict(X_test)\n",
    "y_test_prob = pipe.predict_proba(X_test)[:,1]\n",
    "\n",
    "print(\"accuracy:\", metrics.accuracy_score(y_test, y_test_pred))\n",
    "print(\"precision:\", metrics.precision_score(y_test, y_test_pred))\n",
    "print(\"recall:\", metrics.recall_score(y_test, y_test_pred))\n",
    "print(\"f1_score:\", metrics.f1_score(y_test, y_test_pred))\n",
    "\n",
    "plt.figure(figsize = (8,8))\n",
    "plot_decision_regions(X, y, pipe, X_highlight=X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.9555555555555556\n",
      "precision: 1.0\n",
      "recall: 0.8888888888888888\n",
      "f1_score: 0.9411764705882353\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10b5cc7f0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = np.where(df.Species == \"Iris-versicolor\", 1, 0)\n",
    "X = df[features].values\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y\n",
    "                    , test_size = 0.3, random_state = 1)\n",
    "\n",
    "pipe = pipeline.Pipeline([\n",
    "    #(\"poly\", preprocessing.PolynomialFeatures(degree=4\n",
    "    #                                , include_bias=False)),\n",
    "    #(\"scaler\", preprocessing.StandardScaler()),\n",
    "    (\"est\", neighbors.KNeighborsClassifier(n_neighbors=5))\n",
    "])\n",
    "\n",
    "pipe.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = pipe.predict(X_train)\n",
    "y_test_pred = pipe.predict(X_test)\n",
    "y_test_prob = pipe.predict_proba(X_test)[:,1]\n",
    "\n",
    "print(\"accuracy:\", metrics.accuracy_score(y_test, y_test_pred))\n",
    "print(\"precision:\", metrics.precision_score(y_test, y_test_pred))\n",
    "print(\"recall:\", metrics.recall_score(y_test, y_test_pred))\n",
    "print(\"f1_score:\", metrics.f1_score(y_test, y_test_pred))\n",
    "\n",
    "plt.figure(figsize = (8,8))\n",
    "plot_decision_regions(X, y, pipe, X_highlight=X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.9555555555555556\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a235989e8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = preprocessing.LabelEncoder().fit_transform(df.Species)\n",
    "X = df[features].values\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y\n",
    "                    , test_size = 0.3, random_state = 1)\n",
    "\n",
    "pipe = pipeline.Pipeline([\n",
    "    #(\"poly\", preprocessing.PolynomialFeatures(degree=4\n",
    "    #                                , include_bias=False)),\n",
    "    #(\"scaler\", preprocessing.StandardScaler()),\n",
    "    (\"est\", neighbors.KNeighborsClassifier(n_neighbors=5))\n",
    "])\n",
    "\n",
    "pipe.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = pipe.predict(X_train)\n",
    "y_test_pred = pipe.predict(X_test)\n",
    "y_test_prob = pipe.predict_proba(X_test)[:,1]\n",
    "\n",
    "print(\"accuracy:\", metrics.accuracy_score(y_test, y_test_pred))\n",
    "#print(\"precision:\", metrics.precision_score(y_test, y_test_pred))\n",
    "##print(\"recall:\", metrics.recall_score(y_test, y_test_pred))\n",
    "#print(\"f1_score:\", metrics.f1_score(y_test, y_test_pred))\n",
    "\n",
    "plt.figure(figsize = (8,8))\n",
    "plot_decision_regions(X, y, pipe, X_highlight=X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.9333333333333333\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1ffbbf98>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = preprocessing.LabelEncoder().fit_transform(df.Species)\n",
    "X = df[features].values\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y\n",
    "                    , test_size = 0.3, random_state = 1)\n",
    "\n",
    "pipe = pipeline.Pipeline([\n",
    "    (\"poly\", preprocessing.PolynomialFeatures(degree=3\n",
    "                                    , include_bias=False)),\n",
    "    (\"scaler\", preprocessing.StandardScaler()),\n",
    "    (\"est\", linear_model.LogisticRegression(random_state=1\n",
    "                            , multi_class=\"ovr\"\n",
    "                            , solver = \"liblinear\"))\n",
    "])\n",
    "\n",
    "pipe.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = pipe.predict(X_train)\n",
    "y_test_pred = pipe.predict(X_test)\n",
    "y_test_prob = pipe.predict_proba(X_test)[:,1]\n",
    "\n",
    "print(\"accuracy:\", metrics.accuracy_score(y_test, y_test_pred))\n",
    "#print(\"precision:\", metrics.precision_score(y_test, y_test_pred))\n",
    "##print(\"recall:\", metrics.recall_score(y_test, y_test_pred))\n",
    "#print(\"f1_score:\", metrics.f1_score(y_test, y_test_pred))\n",
    "\n",
    "plt.figure(figsize = (8,8))\n",
    "plot_decision_regions(X, y, pipe, X_highlight=X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
